Today’s announcements build on the more than 50 new Amazon SageMaker capabilities that AWS has delivered in the past year to make it even easier for developers and data scientists to prepare, build, train, deploy, and manage machine learning models, including: Tens of thousands of customers utilize Amazon SageMaker to help accelerate their machine learning deployments, including 3M, ADP, AstraZeneca, Avis, Bayer, Bundesliga, Capital One, Cerner, Chick-fil-A, Convoy, Domino’s Pizza, Fidelity Investments, GE Healthcare, Georgia-Pacific, Hearst, iFood, iHeartMedia, JPMorgan Chase, Intuit, Lenovo, Lyft, National Football League, Nerdwallet, T-Mobile, Thomson Reuters, and Vanguard. However, Amazon SageMaker has changed that. Amazon SageMaker is a fully managed service that removes challenges from each stage of the machine learning process, making it radically easier and faster for everyday developers and data scientists to build, train, and deploy machine learning models. In the past, this process put machine learning out of the reach of all but the most skilled developers. This process needs to be continuously repeated to ensure that the model is performing as expected over time. Then they need to visualize it in notebooks, pick the right algorithm, set up the framework, train the model, tune millions of possible parameters, deploy the model, and monitor its performance. In order to create a model, developers need to start with the highly manual process of preparing the data. With all the attention machine learning has received, it seems like it should be simple to create machine learning models, but it isn’t. Machine learning is becoming more mainstream, but it is still evolving at a rapid clip. To get started with Amazon SageMaker, visit: Today’s announcements bring together powerful new capabilities like faster data preparation, a purpose-built repository for prepared data, workflow automation, greater transparency into training data to mitigate bias and explain predictions, distributed training capabilities to train large models up to two times faster, and model monitoring on edge devices. 8, 2020- Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an , Inc. company (NASDAQ: AMZN), announced nine new capabilities for its industry-leading machine learning service, Amazon SageMaker, making it even easier for developers to automate and scale all steps of the end-to-end machine learning workflow. Amazon SageMaker Data Wrangler provides the fastest and easiest way for developers to prepare data for machine learningĪmazon SageMaker Feature Store delivers a purpose-built data store for storing, updating, retrieving, and sharing machine learning featuresĪmazon SageMaker Pipelines gives developers the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learningĪmazon SageMaker Clarify provides developers with greater visibility into their training data so they can limit bias in machine learning models and explain predictionsĭeep profiling for Amazon SageMaker Debugger monitors machine learning training performance to help developers train models fasterĭistributed Training on Amazon SageMaker delivers new capabilities that can train large models up to two times faster than would otherwise be possible with today’s machine learning processorsĪmazon SageMaker Edge Manager delivers machine learning model monitoring and management for edge devices to ensure that models deployed in production are operating correctlyĪmazon SageMaker JumpStart provides a developer portal for pre-trained models and pre-built workflows
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